Lithium-oxygen batteries (LOBs) offer combustion fuel-like energy densities but remain constrained by low efficiency, limited cycle life, and coupled degradation pathways linking the electrochemical growth and decay of the reaction product (Li2O2) and associated generation of reactive oxygen species, electrolyte and electrode instabilities, and lithium dendrite growth. Here, we introduce a hybrid materials-informatics framework that integrates structured query learning with retrieval-augmented generation (RAG) to systematically analyze the full-text corpus of 3134 peer-reviewed articles in LOBs. Unlike conventional artificial intelligence (AI) tools, which learn from unstructured literature and risk factual drift, the present approach forms a relational performance-validated database, enabling evidence-traceable comparison of cathode architectures, catalyst types, electrolytes, redox mediators, and lithium protection strategies. The analysis reveals composition-dependent performance hierarchies and exposes interdependencies among Li2O2 morphology, singlet-oxygen formation, overpotentials, and solid electrolyte interface disruption, as reported under their documented experimental conditions. Using this method, we identified the catalyst-electrolyte-anode configurations capable of reducing charge polarization by 0.3-0.6 V and extending cycling stability to 100-200 cycles under standard cycling conditions reported in the source studies. This data-driven roadmap establishes a quantitative foundation for translating LOBs from laboratory demonstrations to deployable high-energy systems and demonstrates how materials informatics can accelerate electrochemical materials synthesis and device design.
Sivan et al. (Mon,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: